Comparing Sound-Field Speech-Auditory Brainstem Response Components between Cochlear Implant Users with Different Speech Recognition in Noise Scores

Objectives Many studies have suggested that cochlear implant (CI) users vary in terms of speech recognition in noise. Studies in this field attribute this variety partly to subcortical auditory processing. Studying speech-Auditory Brainstem Response (speech-ABR) provides good information about speech processing; thus, this work was designed to compare speech-ABR components between two groups of CI users with good and poor speech recognition in noise scores. Materials & Methods The present study was conducted on two groups of CI users aged 8-10 years old. The first group (CI-good) consisted of 15 children with prelingual CI who had good speech recognition in noise performance. The second group (CI-poor) was matched with the first group, but they had poor speech recognition in noise performance. The speech-ABR test in a sound-field presentation was performed for all the participants. Results The speech-ABR response showed more delay in C, D, E, F, O latencies in CI-poor than CI-good users (P <0.05), meanwhile no significant difference was observed in initial wave (V(t= -0.293, p= 0.771 and A (t= -1.051, p= 0.307). Analysis in spectral-domain showed a weaker representation of fundamental frequency as well as the first formant and high-frequency component of speech stimuli in the CI users with poor auditory performance. Conclusions Results revealed that CI users who showed poor auditory performance in noise performance had deficits in encoding the periodic portion of speech signals at the brainstem level. Also, this study could be as physiological evidence for poorer pitch processing in CI users with poor speech recognition in noise performance.


Introduction
However, behavioral performance shows the combination of sensory and cognitive processes; thus, in the case of CI users, it is not well understood how variation in speech recognition is related to different specific levels of auditory processing.
Considering speech recognition problems in CI users as well as unique features of this complex sound relative to simpler sounds, such as clicks and tone burst, understanding of how this particular sound is processed in different levels of the central auditory pathway, will likely contribute to our intuition regarding speech recognition problem (2)(3)(4).
Speech sounds are a stream of acoustical elements produced at a rate of three to six syllables per second. Complicated processing is needed to encode these elements and translate them as meaningful words in the cortex. Neural bases of speech perception are primarily located in the cerebral cortex. However, before these sounds are registered and stored in long-term memory, relevant acoustical elements of them must be represented as neural messages encoded through subcortical structure and delivered to the auditory cortex. Regarding the major role of brainstem processing in speech stimuli on the one hand and insufficient information about speech sound processing, especially at the brainstem level in CI users, on the other hand, this study was conducted to investigate how sound stimuli are processed at the brainstem level (5).
Event-related potentials (ERPs) can provide enough information about the neural processing of stimuli at different levels of the auditory pathway (4). Many studies that recorded scalpevoked response to speech sound have suggested that auditory brainstem response shows important features and basic acoustic elements of speech sound. Speech-auditory brainstem response (speech-ABR) as a promising, objective, and noninvasive audiological technique is used for measuring temporal and spectral encoding of a speech sound at brainstem level (6). Speech-ABR is a highly replicable method for the assessment of speech sound processing, and this response is mature by school-age children at five years old (7).
Despite the existence of many complex sounds, /da/ syllabic sound is the most common and well-known speech sound used in more studies.
Brainstem response to speech sound can be used as an index for neural synchronization in an individual with neural impairments. Speech-ABR is known to be language-, music-, experience-, and cognitivedependent (8). Also, other studies have shown a relationship between speech in noise ability and auditory brainstem responses to speech stimuli. These studies have shown that subcortical neural encoding of the speech signal is a key factor for the determination of speech in noise ability (9). A previous study reported that speech-ABR could be used as neural synchrony in impaired subjects, such as individuals with learning impairment, hearing loss, and children with reading problems (10). Among different processing of brainstem structure, phase-locked activity to F0 and formant transition portion in speech-ABR test contribute to the determination of speech recognition in noise ability (11). The capability of the speech-ABR test for measuring neural synchrony and the relationship between speech recognition in noise and processing of sound stimuli in the brainstem motivated us to suppose that speech-ABR could provide a biological marker for CI users with different speech recognition in noise performance.
Thus, it was assumed that poor speech recognition in noise performance results in part from impaired neural encoding, and accordingly, it is expected to observe a correlation of degraded brainstem neural encoding in CI users with different recognition in noise performance (9). Therefore, this study was designed to compare sound-field speech-ABR components between two groups of CI users with different speech recognition in noise performance to test the hypothesis that CI user with poor speech recognition in noise performance has specific dysfunction at the brainstem level.

Materials & Methods
In the current study, 30 unilateral CI users aged This group included CI users with bilateral profound congenital sensory-neural hearing loss before implantation who were not successfully treated with a hearing aid for at least six months.
These participants used a nucleus prosthesis (CI24RE) and advanced combination encoder (ACE) processing strategy with an omnidirectional microphone in the right ear for at least three years.
All children were -monolingual, right-handed, and had no history of head trauma, cognitive problems, neurologic impairment, growth-related diseases, and psychological disorders. The second group (CI-poor) was matched with the first group, but they had poor speech recognition of words in noise according to the results of the PARWIN test.
Children who were unwilling to cooperate and perform the tests, as well as those with general health problems and conductive disorders, were excluded from the study.
Mean and standard deviation of the chronological age of two groups of CI users, age at the time of implantation, duration of CI usage, and the age of identification of hearing loss are presented in Table   1.  device was used to measure speech-ABR test. Each participant was instructed at the beginning of the test, and they were asked to sit quietly on a chair and do not talk or move. To achieve decreased physical movements and more relaxation, a mute animation movie was displayed for them on the screen placed in front of them. Ag-Excl electrodes were located on the skull for recording auditory evoked potentials. To decrease electrodes' impedance, the place of electrodes was cleaned using skin cleanser gel. For a better recording of evoked potentials, the impedance of the electrodes was kept less than 5 kΩ during the test, and the inter-electrode difference was set below than 3 kΩ. The intensity of the stimuli was presented at

Behavioral Measures
The mean of word discrimination scores in quiet was equal to 72.93% ±6.88 and 68.93% ±5.89 for CI-good and CI-poor groups, respectively.
There was no significant difference in word discrimination scores in the quiet between the two groups of CI users. Descriptive statistics, including mean and SD for hearing level threshold at 500, 1k, 2k, and 4 kHz, WDS, and PARWIN test for two groups of CI users are outlined in Table 1.
Comparison of the results of the PARWIN test scores in the two groups revealed that CI-good group achieved lower SNR ratios or better auditory performance in noise for discrimination of words in noise than CI-poor group. These results showed a significant difference between the two groups of CI users after adding the noise (P <0.05) ( Figure   2).

Sound-Field Speech-ABR Measures
Results of independent samples t-test revealed that the CI-poor group had longer absolute latency for sustained and offset peaks of the speech-ABR test compared to CI-good users (p<0.05). Analysis of transient portion of response showed longer latency of V and A peaks in CI-poor group, but these differences were not statically significant between the two groups. The t-and p-values were obtained as -0.293, p= 0.771 and t= -1.051, p= 0.307, respectively for V and A peaks between the two groups. For deeper assessment, duration, amplitude, slope, and area of V/A complex were measured as indices of onset responses.
Independent samples t-test indicated no significant differences in A/V, duration, and slope between the two groups of CI users. The mean, SD, and p-value of all peaks of speech-ABR are shown in Table 2.
The grand average of sound-field speech-ABR waveforms for children in CI-good and CI-poor groups are shown in Figure 3.
The results showed that the amplitude of the FFR portion, including D, E, and F waves, was significantly lower in the CI-poor group than CIgood group; however, analyses of other waves showed that there was no significant difference in initial waves (V and A), transition part (C), and offset of response. Mean, SD, and p-value for the amplitude of waves in the two groups are shown in Table 2 (25,26). Therefore, considering these results, the difference observed in speech recognition in noise performance between two groups of CI users in this study may be attributable to at least in part different processing of fundamental frequency of speech stimuli at brainstem level (27).

In conclusion
the results of the study revealed that CI users who showed poor auditory performance, especially in noise, had deficits in the encoding of the periodic portion of speech signals at the brainstem level.
Also, pitch processing, including F0, H1, and HF was weaker in CI users with poor speech recognition in noise performance than those with good performance. Auditory evoked potentials, such as speech-ABR test as objective, reliable, and fast method would be useful for determining the CI users who show abnormality in speech processing at the brainstem level. Administrating auditory training based on speech in noise program and monitoring by the speech-ABR test is recommended for CI users in future research, especially those with poor speech recognition in noise performance.